import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import geopandas as gpd
%matplotlib inline
# dünya onaylanmış vaka sayıları
confirmed = pd.read_csv('confirmed.csv')
# dünya ölüm sayıları
deaths = pd.read_csv('deaths.csv')
# dünya iyileşen sayıları
recovered = pd.read_csv('recovered.csv')
confirmed.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 276 entries, 0 to 275 Columns: 503 entries, Province/State to 6/3/21 dtypes: float64(2), int64(499), object(2) memory usage: 1.1+ MB
deaths.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 276 entries, 0 to 275 Columns: 503 entries, Province/State to 6/3/21 dtypes: float64(2), int64(499), object(2) memory usage: 1.1+ MB
recovered.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 261 entries, 0 to 260 Columns: 503 entries, Province/State to 6/3/21 dtypes: float64(2), int64(499), object(2) memory usage: 1.0+ MB
confirmed.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Lat | 274.0 | 20.447559 | 2.518984e+01 | -51.7963 | 4.933349 | 21.607878 | 40.950592 | 7.170690e+01 |
| Long | 274.0 | 22.328281 | 7.436910e+01 | -178.1165 | -22.036550 | 20.921188 | 83.380449 | 1.780650e+02 |
| 1/22/20 | 276.0 | 2.018116 | 2.678174e+01 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 4.440000e+02 |
| 1/23/20 | 276.0 | 2.373188 | 2.687957e+01 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 4.440000e+02 |
| 1/24/20 | 276.0 | 3.409420 | 3.346416e+01 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 5.490000e+02 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5/30/21 | 276.0 | 617194.481884 | 2.890908e+06 | 0.0000 | 1352.250000 | 19177.000000 | 254386.250000 | 3.326173e+07 |
| 5/31/21 | 276.0 | 618565.985507 | 2.896575e+06 | 0.0000 | 1352.250000 | 19097.500000 | 254530.250000 | 3.326751e+07 |
| 6/1/21 | 276.0 | 620244.927536 | 2.904189e+06 | 0.0000 | 1352.250000 | 19100.000000 | 254962.750000 | 3.329045e+07 |
| 6/2/21 | 276.0 | 622031.927536 | 2.911920e+06 | 0.0000 | 1356.500000 | 19102.000000 | 255755.500000 | 3.330736e+07 |
| 6/3/21 | 276.0 | 623804.090580 | 2.919518e+06 | 0.0000 | 1358.750000 | 19108.500000 | 256173.750000 | 3.332644e+07 |
501 rows × 8 columns
deaths.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Lat | 274.0 | 20.447559 | 25.189838 | -51.7963 | 4.933349 | 21.607878 | 40.950592 | 71.7069 |
| Long | 274.0 | 22.328281 | 74.369096 | -178.1165 | -22.036550 | 20.921188 | 83.380449 | 178.0650 |
| 1/22/20 | 276.0 | 0.061594 | 1.023280 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 17.0000 |
| 1/23/20 | 276.0 | 0.065217 | 1.024830 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 17.0000 |
| 1/24/20 | 276.0 | 0.094203 | 1.446690 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 24.0000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5/30/21 | 276.0 | 12830.931159 | 54032.103939 | 0.0000 | 8.750000 | 268.000000 | 3528.250000 | 594443.0000 |
| 5/31/21 | 276.0 | 12862.380435 | 54137.898125 | 0.0000 | 8.750000 | 270.000000 | 3529.750000 | 594585.0000 |
| 6/1/21 | 276.0 | 12917.083333 | 54378.469075 | 0.0000 | 8.750000 | 275.000000 | 3535.750000 | 595223.0000 |
| 6/2/21 | 276.0 | 13375.594203 | 55428.221463 | 0.0000 | 8.750000 | 276.000000 | 3539.500000 | 595833.0000 |
| 6/3/21 | 276.0 | 13411.496377 | 55574.306719 | 0.0000 | 8.750000 | 283.500000 | 3542.250000 | 596434.0000 |
501 rows × 8 columns
recovered.describe().T
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Lat | 260.0 | 19.016304 | 2.463273e+01 | -51.7963 | 4.562000 | 19.584785 | 38.991325 | 7.170690e+01 |
| Long | 260.0 | 27.737245 | 7.184781e+01 | -178.1165 | -9.496274 | 24.242250 | 90.375625 | 1.780650e+02 |
| 1/22/20 | 261.0 | 0.114943 | 1.737097e+00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 2.800000e+01 |
| 1/23/20 | 261.0 | 0.122605 | 1.740995e+00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 2.800000e+01 |
| 1/24/20 | 261.0 | 0.149425 | 1.932687e+00 | 0.0000 | 0.000000 | 0.000000 | 0.000000 | 3.100000e+01 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5/30/21 | 261.0 | 412377.770115 | 1.932424e+06 | 0.0000 | 1039.000000 | 13248.000000 | 191475.000000 | 2.569234e+07 |
| 5/31/21 | 261.0 | 414405.237548 | 1.947267e+06 | 0.0000 | 1039.000000 | 13255.000000 | 192112.000000 | 2.594763e+07 |
| 6/1/21 | 261.0 | 416642.337165 | 1.962782e+06 | 0.0000 | 1039.000000 | 13256.000000 | 192823.000000 | 2.617908e+07 |
| 6/2/21 | 261.0 | 418666.233716 | 1.976202e+06 | 0.0000 | 1040.000000 | 13256.000000 | 193491.000000 | 2.639058e+07 |
| 6/3/21 | 261.0 | 420309.950192 | 1.987374e+06 | 0.0000 | 1041.000000 | 13260.000000 | 194291.000000 | 2.659766e+07 |
501 rows × 8 columns
confirmed
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | 5/30/21 | 5/31/21 | 6/1/21 | 6/2/21 | 6/3/21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.939110 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 66903 | 67743 | 68366 | 69130 | 70111 | 70761 | 71838 | 72977 | 74026 | 75119 |
| 1 | NaN | Albania | 41.153300 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 132229 | 132244 | 132264 | 132285 | 132297 | 132309 | 132315 | 132337 | 132351 | 132360 |
| 2 | NaN | Algeria | 28.033900 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 127361 | 127646 | 127926 | 128198 | 128456 | 128725 | 128913 | 129218 | 129640 | 129976 |
| 3 | NaN | Andorra | 42.506300 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 13664 | 13671 | 13682 | 13693 | 13693 | 13693 | 13727 | 13729 | 13744 | 13752 |
| 4 | NaN | Angola | -11.202700 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 32933 | 33338 | 33607 | 33944 | 34180 | 34366 | 34551 | 34752 | 34960 | 35140 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | NaN | Vietnam | 14.058324 | 108.277199 | 0 | 2 | 2 | 2 | 2 | 2 | ... | 5931 | 6086 | 6356 | 6396 | 6908 | 7107 | 7432 | 7625 | 7870 | 8063 |
| 272 | NaN | West Bank and Gaza | 31.952200 | 35.233200 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 306334 | 306795 | 306795 | 307569 | 307838 | 308048 | 308350 | 308732 | 309036 | 309333 |
| 273 | NaN | Yemen | 15.552727 | 48.516388 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6670 | 6688 | 6696 | 6723 | 6731 | 6737 | 6742 | 6751 | 6759 | 6767 |
| 274 | NaN | Zambia | -13.133897 | 27.849332 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 93428 | 93627 | 93947 | 94430 | 94751 | 95050 | 95263 | 95821 | 96563 | 97388 |
| 275 | NaN | Zimbabwe | -19.015438 | 29.154857 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 38706 | 38819 | 38854 | 38918 | 38933 | 38944 | 38961 | 38998 | 39031 | 39092 |
276 rows × 503 columns
deaths
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | 5/30/21 | 5/31/21 | 6/1/21 | 6/2/21 | 6/3/21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.939110 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 2836 | 2855 | 2869 | 2881 | 2899 | 2919 | 2944 | 2973 | 3007 | 3034 |
| 1 | NaN | Albania | 41.153300 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 2447 | 2447 | 2447 | 2448 | 2449 | 2450 | 2451 | 2451 | 2451 | 2451 |
| 2 | NaN | Algeria | 28.033900 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 3433 | 3440 | 3448 | 3455 | 3460 | 3465 | 3472 | 3480 | 3490 | 3497 |
| 3 | NaN | Andorra | 42.506300 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 127 | 127 | 127 | 127 | 127 | 127 | 127 | 127 | 127 | 127 |
| 4 | NaN | Angola | -11.202700 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 735 | 742 | 745 | 749 | 757 | 764 | 766 | 772 | 780 | 784 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | NaN | Vietnam | 14.058324 | 108.277199 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 44 | 45 | 46 | 47 | 47 | 47 | 47 | 48 | 49 | 49 |
| 272 | NaN | West Bank and Gaza | 31.952200 | 35.233200 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 3480 | 3483 | 3483 | 3489 | 3492 | 3495 | 3497 | 3503 | 3507 | 3509 |
| 273 | NaN | Yemen | 15.552727 | 48.516388 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1311 | 1313 | 1315 | 1316 | 1319 | 1320 | 1321 | 1322 | 1323 | 1325 |
| 274 | NaN | Zambia | -13.133897 | 27.849332 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1271 | 1273 | 1275 | 1275 | 1276 | 1278 | 1281 | 1282 | 1284 | 1288 |
| 275 | NaN | Zimbabwe | -19.015438 | 29.154857 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 1587 | 1589 | 1592 | 1592 | 1594 | 1594 | 1594 | 1599 | 1599 | 1604 |
276 rows × 503 columns
recovered
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | 5/30/21 | 5/31/21 | 6/1/21 | 6/2/21 | 6/3/21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.939110 | 67.709953 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 56518 | 56711 | 56962 | 57119 | 57281 | 57450 | 57629 | 57741 | 57963 | 58070 |
| 1 | NaN | Albania | 41.153300 | 20.168300 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 128907 | 128978 | 129042 | 129097 | 129215 | 129308 | 129431 | 129473 | 129521 | 129566 |
| 2 | NaN | Algeria | 28.033900 | 1.659600 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 88672 | 88861 | 89040 | 89232 | 89419 | 89625 | 89839 | 90057 | 90281 | 90517 |
| 3 | NaN | Andorra | 42.506300 | 1.521800 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 13263 | 13381 | 13405 | 13416 | 13416 | 13416 | 13458 | 13479 | 13507 | 13527 |
| 4 | NaN | Angola | -11.202700 | 17.873900 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 27204 | 27467 | 27529 | 27577 | 27646 | 27766 | 28079 | 28190 | 28264 | 28646 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 256 | NaN | Vietnam | 14.058324 | 108.277199 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 2794 | 2853 | 2853 | 2896 | 2896 | 2950 | 3029 | 3043 | 3085 | 3085 |
| 257 | NaN | West Bank and Gaza | 31.952200 | 35.233200 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 299024 | 299559 | 299559 | 300125 | 300524 | 300661 | 300776 | 300919 | 301213 | 301443 |
| 258 | NaN | Yemen | 15.552727 | 48.516388 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 3273 | 3306 | 3339 | 3375 | 3399 | 3427 | 3445 | 3472 | 3472 | 3484 |
| 259 | NaN | Zambia | -13.133897 | 27.849332 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 91221 | 91239 | 91321 | 91443 | 91594 | 91752 | 91956 | 92039 | 92108 | 92320 |
| 260 | NaN | Zimbabwe | -19.015438 | 29.154857 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 36517 | 36531 | 36541 | 36563 | 36578 | 36591 | 36594 | 36624 | 36661 | 36680 |
261 rows × 503 columns
confirmed = confirmed.drop(columns=["Province/State","Lat","Long"])
deaths = deaths.drop(columns=["Province/State","Lat","Long"])
recovered = recovered.drop(columns=["Province/State","Lat","Long"])
confirmed
| Country/Region | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | ... | 5/25/21 | 5/26/21 | 5/27/21 | 5/28/21 | 5/29/21 | 5/30/21 | 5/31/21 | 6/1/21 | 6/2/21 | 6/3/21 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 66903 | 67743 | 68366 | 69130 | 70111 | 70761 | 71838 | 72977 | 74026 | 75119 |
| 1 | Albania | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 132229 | 132244 | 132264 | 132285 | 132297 | 132309 | 132315 | 132337 | 132351 | 132360 |
| 2 | Algeria | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 127361 | 127646 | 127926 | 128198 | 128456 | 128725 | 128913 | 129218 | 129640 | 129976 |
| 3 | Andorra | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 13664 | 13671 | 13682 | 13693 | 13693 | 13693 | 13727 | 13729 | 13744 | 13752 |
| 4 | Angola | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 32933 | 33338 | 33607 | 33944 | 34180 | 34366 | 34551 | 34752 | 34960 | 35140 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 271 | Vietnam | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | ... | 5931 | 6086 | 6356 | 6396 | 6908 | 7107 | 7432 | 7625 | 7870 | 8063 |
| 272 | West Bank and Gaza | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 306334 | 306795 | 306795 | 307569 | 307838 | 308048 | 308350 | 308732 | 309036 | 309333 |
| 273 | Yemen | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 6670 | 6688 | 6696 | 6723 | 6731 | 6737 | 6742 | 6751 | 6759 | 6767 |
| 274 | Zambia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 93428 | 93627 | 93947 | 94430 | 94751 | 95050 | 95263 | 95821 | 96563 | 97388 |
| 275 | Zimbabwe | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 38706 | 38819 | 38854 | 38918 | 38933 | 38944 | 38961 | 38998 | 39031 | 39092 |
276 rows × 500 columns
confirmed=confirmed.groupby("Country/Region").aggregate(np.sum).T
deaths=deaths.groupby("Country/Region").aggregate(np.sum).T
recovered=recovered.groupby("Country/Region").aggregate(np.sum).T
confirmed
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | ... | United Kingdom | Uruguay | Uzbekistan | Vanuatu | Venezuela | Vietnam | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1/22/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1/23/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 1/24/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 1/25/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 1/26/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5/30/21 | 70761 | 132309 | 128725 | 13693 | 34366 | 1259 | 3753609 | 222636 | 30105 | 644586 | ... | 4499937 | 291488 | 100124 | 4 | 232800 | 7107 | 308048 | 6737 | 95050 | 38944 |
| 5/31/21 | 71838 | 132315 | 128913 | 13727 | 34551 | 1260 | 3781784 | 222670 | 30118 | 644815 | ... | 4503231 | 294503 | 100335 | 4 | 234165 | 7432 | 308350 | 6742 | 95263 | 38961 |
| 6/1/21 | 72977 | 132337 | 129218 | 13729 | 34752 | 1260 | 3817139 | 222778 | 30124 | 645152 | ... | 4506333 | 298006 | 100495 | 4 | 235567 | 7625 | 308732 | 6751 | 95821 | 38998 |
| 6/2/21 | 74026 | 132351 | 129640 | 13744 | 34960 | 1262 | 3852156 | 222870 | 30137 | 645552 | ... | 4510597 | 301524 | 100726 | 4 | 236755 | 7870 | 309036 | 6759 | 96563 | 39031 |
| 6/3/21 | 75119 | 132360 | 129976 | 13752 | 35140 | 1262 | 3884447 | 222978 | 30141 | 645834 | ... | 4515778 | 304411 | 100997 | 4 | 238013 | 8063 | 309333 | 6767 | 97388 | 39092 |
499 rows × 193 columns
confirmed.index.name="Date"
deaths.index.name="Date"
recovered.index.name="Date"
confirmed
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | ... | United Kingdom | Uruguay | Uzbekistan | Vanuatu | Venezuela | Vietnam | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||||||||
| 1/22/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1/23/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 1/24/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 1/25/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 1/26/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5/30/21 | 70761 | 132309 | 128725 | 13693 | 34366 | 1259 | 3753609 | 222636 | 30105 | 644586 | ... | 4499937 | 291488 | 100124 | 4 | 232800 | 7107 | 308048 | 6737 | 95050 | 38944 |
| 5/31/21 | 71838 | 132315 | 128913 | 13727 | 34551 | 1260 | 3781784 | 222670 | 30118 | 644815 | ... | 4503231 | 294503 | 100335 | 4 | 234165 | 7432 | 308350 | 6742 | 95263 | 38961 |
| 6/1/21 | 72977 | 132337 | 129218 | 13729 | 34752 | 1260 | 3817139 | 222778 | 30124 | 645152 | ... | 4506333 | 298006 | 100495 | 4 | 235567 | 7625 | 308732 | 6751 | 95821 | 38998 |
| 6/2/21 | 74026 | 132351 | 129640 | 13744 | 34960 | 1262 | 3852156 | 222870 | 30137 | 645552 | ... | 4510597 | 301524 | 100726 | 4 | 236755 | 7870 | 309036 | 6759 | 96563 | 39031 |
| 6/3/21 | 75119 | 132360 | 129976 | 13752 | 35140 | 1262 | 3884447 | 222978 | 30141 | 645834 | ... | 4515778 | 304411 | 100997 | 4 | 238013 | 8063 | 309333 | 6767 | 97388 | 39092 |
499 rows × 193 columns
confirmed=confirmed.reset_index()
deaths=deaths.reset_index()
recovered=recovered.reset_index()
confirmed
| Country/Region | Date | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | ... | United Kingdom | Uruguay | Uzbekistan | Vanuatu | Venezuela | Vietnam | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1/22/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 1/23/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 2 | 1/24/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 3 | 1/25/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 4 | 1/26/20 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 494 | 5/30/21 | 70761 | 132309 | 128725 | 13693 | 34366 | 1259 | 3753609 | 222636 | 30105 | ... | 4499937 | 291488 | 100124 | 4 | 232800 | 7107 | 308048 | 6737 | 95050 | 38944 |
| 495 | 5/31/21 | 71838 | 132315 | 128913 | 13727 | 34551 | 1260 | 3781784 | 222670 | 30118 | ... | 4503231 | 294503 | 100335 | 4 | 234165 | 7432 | 308350 | 6742 | 95263 | 38961 |
| 496 | 6/1/21 | 72977 | 132337 | 129218 | 13729 | 34752 | 1260 | 3817139 | 222778 | 30124 | ... | 4506333 | 298006 | 100495 | 4 | 235567 | 7625 | 308732 | 6751 | 95821 | 38998 |
| 497 | 6/2/21 | 74026 | 132351 | 129640 | 13744 | 34960 | 1262 | 3852156 | 222870 | 30137 | ... | 4510597 | 301524 | 100726 | 4 | 236755 | 7870 | 309036 | 6759 | 96563 | 39031 |
| 498 | 6/3/21 | 75119 | 132360 | 129976 | 13752 | 35140 | 1262 | 3884447 | 222978 | 30141 | ... | 4515778 | 304411 | 100997 | 4 | 238013 | 8063 | 309333 | 6767 | 97388 | 39092 |
499 rows × 194 columns
confirmed=confirmed.melt(id_vars=["Date"],var_name="Country",value_name="Confirmed")
deaths=deaths.melt(id_vars=["Date"],var_name="Country",value_name="Deaths")
recovered=recovered.melt(id_vars=["Date"],var_name="Country",value_name="Recovered")
confirmed
| Date | Country | Confirmed | |
|---|---|---|---|
| 0 | 1/22/20 | Afghanistan | 0 |
| 1 | 1/23/20 | Afghanistan | 0 |
| 2 | 1/24/20 | Afghanistan | 0 |
| 3 | 1/25/20 | Afghanistan | 0 |
| 4 | 1/26/20 | Afghanistan | 0 |
| ... | ... | ... | ... |
| 96302 | 5/30/21 | Zimbabwe | 38944 |
| 96303 | 5/31/21 | Zimbabwe | 38961 |
| 96304 | 6/1/21 | Zimbabwe | 38998 |
| 96305 | 6/2/21 | Zimbabwe | 39031 |
| 96306 | 6/3/21 | Zimbabwe | 39092 |
96307 rows × 3 columns
deaths
| Date | Country | Deaths | |
|---|---|---|---|
| 0 | 1/22/20 | Afghanistan | 0 |
| 1 | 1/23/20 | Afghanistan | 0 |
| 2 | 1/24/20 | Afghanistan | 0 |
| 3 | 1/25/20 | Afghanistan | 0 |
| 4 | 1/26/20 | Afghanistan | 0 |
| ... | ... | ... | ... |
| 96302 | 5/30/21 | Zimbabwe | 1594 |
| 96303 | 5/31/21 | Zimbabwe | 1594 |
| 96304 | 6/1/21 | Zimbabwe | 1599 |
| 96305 | 6/2/21 | Zimbabwe | 1599 |
| 96306 | 6/3/21 | Zimbabwe | 1604 |
96307 rows × 3 columns
recovered
| Date | Country | Recovered | |
|---|---|---|---|
| 0 | 1/22/20 | Afghanistan | 0 |
| 1 | 1/23/20 | Afghanistan | 0 |
| 2 | 1/24/20 | Afghanistan | 0 |
| 3 | 1/25/20 | Afghanistan | 0 |
| 4 | 1/26/20 | Afghanistan | 0 |
| ... | ... | ... | ... |
| 96302 | 5/30/21 | Zimbabwe | 36591 |
| 96303 | 5/31/21 | Zimbabwe | 36594 |
| 96304 | 6/1/21 | Zimbabwe | 36624 |
| 96305 | 6/2/21 | Zimbabwe | 36661 |
| 96306 | 6/3/21 | Zimbabwe | 36680 |
96307 rows × 3 columns
confirmed["Date"]=pd.to_datetime(confirmed["Date"])
deaths["Date"]=pd.to_datetime(deaths["Date"])
recovered["Date"]=pd.to_datetime(recovered["Date"])
confirmed["Date"]=confirmed["Date"].dt.strftime("%Y/%m/%d")
deaths["Date"]=deaths["Date"].dt.strftime("%Y/%m/%d")
recovered["Date"]=recovered["Date"].dt.strftime("%Y/%m/%d")
confirmed
| Date | Country | Confirmed | |
|---|---|---|---|
| 0 | 2020/01/22 | Afghanistan | 0 |
| 1 | 2020/01/23 | Afghanistan | 0 |
| 2 | 2020/01/24 | Afghanistan | 0 |
| 3 | 2020/01/25 | Afghanistan | 0 |
| 4 | 2020/01/26 | Afghanistan | 0 |
| ... | ... | ... | ... |
| 96302 | 2021/05/30 | Zimbabwe | 38944 |
| 96303 | 2021/05/31 | Zimbabwe | 38961 |
| 96304 | 2021/06/01 | Zimbabwe | 38998 |
| 96305 | 2021/06/02 | Zimbabwe | 39031 |
| 96306 | 2021/06/03 | Zimbabwe | 39092 |
96307 rows × 3 columns
deaths
| Date | Country | Deaths | |
|---|---|---|---|
| 0 | 2020/01/22 | Afghanistan | 0 |
| 1 | 2020/01/23 | Afghanistan | 0 |
| 2 | 2020/01/24 | Afghanistan | 0 |
| 3 | 2020/01/25 | Afghanistan | 0 |
| 4 | 2020/01/26 | Afghanistan | 0 |
| ... | ... | ... | ... |
| 96302 | 2021/05/30 | Zimbabwe | 1594 |
| 96303 | 2021/05/31 | Zimbabwe | 1594 |
| 96304 | 2021/06/01 | Zimbabwe | 1599 |
| 96305 | 2021/06/02 | Zimbabwe | 1599 |
| 96306 | 2021/06/03 | Zimbabwe | 1604 |
96307 rows × 3 columns
recovered
| Date | Country | Recovered | |
|---|---|---|---|
| 0 | 2020/01/22 | Afghanistan | 0 |
| 1 | 2020/01/23 | Afghanistan | 0 |
| 2 | 2020/01/24 | Afghanistan | 0 |
| 3 | 2020/01/25 | Afghanistan | 0 |
| 4 | 2020/01/26 | Afghanistan | 0 |
| ... | ... | ... | ... |
| 96302 | 2021/05/30 | Zimbabwe | 36591 |
| 96303 | 2021/05/31 | Zimbabwe | 36594 |
| 96304 | 2021/06/01 | Zimbabwe | 36624 |
| 96305 | 2021/06/02 | Zimbabwe | 36661 |
| 96306 | 2021/06/03 | Zimbabwe | 36680 |
96307 rows × 3 columns
max_date=confirmed["Date"].max()
print(type(max_date))
max_date
<class 'str'>
'2021/06/03'
max_confirmed=confirmed[confirmed["Date"]==max_date]
max_deaths=deaths[deaths["Date"]==max_date]
max_recovered=recovered[recovered["Date"]==max_date]
max_confirmed
| Date | Country | Confirmed | |
|---|---|---|---|
| 498 | 2021/06/03 | Afghanistan | 75119 |
| 997 | 2021/06/03 | Albania | 132360 |
| 1496 | 2021/06/03 | Algeria | 129976 |
| 1995 | 2021/06/03 | Andorra | 13752 |
| 2494 | 2021/06/03 | Angola | 35140 |
| ... | ... | ... | ... |
| 94310 | 2021/06/03 | Vietnam | 8063 |
| 94809 | 2021/06/03 | West Bank and Gaza | 309333 |
| 95308 | 2021/06/03 | Yemen | 6767 |
| 95807 | 2021/06/03 | Zambia | 97388 |
| 96306 | 2021/06/03 | Zimbabwe | 39092 |
193 rows × 3 columns
total_confirmed=max_confirmed["Confirmed"].sum()
total_confirmed
172169929
total_deaths=deaths["Deaths"].sum()
total_deaths
642156210
total_recovered=recovered["Recovered"].sum()
total_recovered
15989001302
fig=go.Figure()
fig.add_trace(go.Indicator(mode="number",value=int(total_confirmed),number={"valueformat":"0.f"},
title={"text":"Toplam Vaka"},domain={"row":0,"column":0}))
fig.add_trace(go.Indicator(mode="number",value=int(total_deaths),number={"valueformat":"0.f"},
title={"text":"Toplam Ölüm"},domain={"row":0,"column":1}))
fig.add_trace(go.Indicator(mode="number",value=int(total_recovered),number={"valueformat":"0.f"},
title={"text":"Toplam İyileşen"},domain={"row":1,"column":0}))
fig.update_layout(grid={"rows":2,"columns":2,"pattern":"independent"})
fig.show()
fig=px.bar(max_confirmed,x="Country",y="Confirmed", title="Ülkelere Göre Vaka Sayıları")
fig.update_layout(
title={
'text': "Ülkelere Göre Vaka Sayıları",
'y':0.9,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
fig.show()
fig=px.choropleth(max_confirmed,locations="Country",locationmode="country names",
color_continuous_scale="dense",
color=np.log10(max_confirmed["Confirmed"]),range_color=(0,9))
fig.show()
import plotly.offline as py
py.init_notebook_mode(connected=True)
countries = np.unique(confirmed['Country'])
mean_conf = []
for country in countries:
mean_conf.append(confirmed[confirmed['Country'] == country]['Confirmed'].sum())
data = [ dict(
type = 'choropleth',
locations = countries,
z = mean_conf,
locationmode = 'country names',
text = countries,
marker = dict(
line = dict(color = 'rgb(0,0,0)', width = 1)),
colorbar = dict(autotick = True, tickprefix = '',
title = 'Count')
)
]
layout = dict(
title = 'Vaka Sayıları',
geo = dict(
showframe = False,
showocean = True,
oceancolor = 'rgb(0,255,255)',
projection = dict(
type = 'orthographic',
rotation = dict(
lon = 60,
lat = 10),
),
lonaxis = dict(
showgrid = True,
gridcolor = 'rgb(102, 102, 102)'
),
lataxis = dict(
showgrid = True,
gridcolor = 'rgb(102, 102, 102)'
)
),
)
fig = dict(data=data, layout=layout)
py.iplot(fig, validate=False, filename='worldmap')
fig=px.scatter(confirmed,x="Date",y="Confirmed",color="Country")
fig.update_traces(opacity=0.6)
fig.update_layout(
title={
'text': "Zamana Göre Vaka Sayıları",
'y':0.95,
'x':0.55,
'xanchor': 'center',
'yanchor': 'top'})
fig.show()
fig=px.bar(max_confirmed.sort_values("Confirmed",ascending=False).head(10),x="Country",y="Confirmed")
fig.update_layout(
title={
'text': "Vaka Sayısı En Yüksek 10 Ülke",
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'})
fig.show()
fig = go.Figure()
fig.add_trace(go.Scatter(
x=confirmed.Country,
y=confirmed['Confirmed'],
name="Confirmed",
line_color='deepskyblue',
opacity=0.8))
fig.add_trace(go.Scatter(
x=recovered.Country,
y=recovered['Recovered'],
name="Recovered",
line_color='dimgray',
opacity=0.8))
fig.update_layout( title={
'text': "Ülkelerin Vaka/İyileşen Grafiği",
'y':0.95,
'x':0.5,
'xanchor': 'center',
'yanchor': 'top'},
xaxis_rangeslider_visible=True)
fig.show()
figure=px.line(confirmed[confirmed["Country"]=="Turkey"],x="Date",y="Confirmed")
figure
series_confirmed = pd.Series.to_list(max_confirmed['Confirmed'])
series_deaths = pd.Series.to_list(max_deaths['Deaths'])
series_recovered = pd.Series.to_list(max_recovered['Recovered'])
np_confirmed = np.array(series_confirmed)
np_deaths = np.array(series_deaths)
np_recovered = np.array(series_recovered)